"""
Plot Spyogenes subplots ms_bokeh
=======================================

Here we show how we can plot multiple chromatograms across runs together
"""

import pandas as pd
import numpy as np
from bokeh.layouts import column
from bokeh.io import show
from pyopenms_viz.util import download_file, unzip_file

# GitHub release asset (primary) with Zenodo as backup
url = (
    "https://github.com/OpenMS/pyopenms_viz/releases/download/manuscript/spyogenes.zip"
)
backup_url = "https://zenodo.org/records/17904512/files/spyogenes.zip?download=1"
zip_filename = "spyogenes.zip"
zip_dir = "spyogenes"

download_file(url, zip_filename, backup_url=backup_url)
unzip_file(zip_filename, ".")  # Extract to current directory

annotation_bounds = pd.read_csv(
    "spyogenes/AADGQTVSGGSILYR3_manual_annotations.tsv", sep="\t"
)  # contain annotations across all runs
chrom_df = pd.read_csv(
    "spyogenes/chroms_AADGQTVSGGSILYR3.tsv", sep="\t"
)  # contains chromatogram for precursor across all runs

##### Set Plotting Variables #####
pd.options.plotting.backend = "ms_bokeh"
RUN_NAMES = [
    "Run #0 Spyogenes 0% human plasma",
    "Run #1 Spyogenes 0% human plasma",
    "Run #2 Spyogenes 0% human plasma",
    "Run #3 Spyogenes 10% human plasma",
    "Run #4 Spyogenes 10% human plasma",
    "Run #5 Spyogenes 10% human plasma",
]

# For each run fill in the axs object with the corresponding chromatogram
plot_list = []
for i, run in enumerate(RUN_NAMES):
    run_df = chrom_df[chrom_df["run_name"] == run]
    current_bounds = annotation_bounds[annotation_bounds["run"] == run]

    plot_list.append(
        run_df.plot(
            kind="chromatogram",
            x="rt",
            y="int",
            grid=False,
            by="ion_annotation",
            title=run_df.iloc[0]["run_name"],
            title_font_size=16,
            width=700,
            xaxis_label_font_size=16,
            yaxis_label_font_size=16,
            xaxis_tick_font_size=14,
            yaxis_tick_font_size=14,
            relative_intensity=True,
            annotation_data=current_bounds,
            xlabel="Retention Time (sec)",
            ylabel="Relative\nIntensity",
            show_plot=False,
            legend_config={"show": True, "title": "Transition"},
        )
    )

show(column(plot_list))